integrate quality by design (qbd) into process validation · 1 integrate quality by design (qbd)...

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Integrate Quality by Design (QbD) into Process Validation

Sanjay Sharma.Sr. General Manager & Head Technology Transfer

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• QbD in Product Lifecycle – An Overview

• QbD Enablers

• Establishing Control Strategy in Product Lifecycle

• Quality Metrics in Process Validation

• QbD case studies

Agenda

3


• QbD Enablers




Agenda

4

QbD in Product Lifecycle – An Overview

• Product Lifecycle Pathway

• Conceptual application of QbD through out a products lifecycle.

5

DEVELOPMENT EXHIBITFILING AND APPROVAL

VALIDATION AND LAUNCH

POST LAUNCH

FTO

PRODUCT LIFECYCLE

Stage 1Process Design

Stage 2Process Qualification

Stage 3Continuous Process

Verification

Product Lifecycle – Pathway

6

FTO

QbD approachQTPP, CTQ (CQA,

CPP and CMA)

Design Space & Control Strategy

F

I

L

I

N

G

Stage 1Process Design


7

FTO


Risk Assessment

post deficiency response

Scale up &

PPQ Batches

Recommendation for CPV

F

I

L

I

N

G

D

E

F

I

C

I

E

N

C

Y

C

P

V

Stage 2Process Qualification

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Approach Followed for calculating no of PPQ batches

- The Bayesian method can be used to determine the number of validation batches required for stage2 PPQ.

- Process Performance data from stage1 are modelled through Beta error distribution and combined with expected outcomes of stage2 PPQ to derive posterior probability for future batches to meet specifications.


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Sample Data – Product A • Batch wise dissolution data:

Test Specification UnitsBatch 1Pre EB

Batch 2EB1

Batch 3EB2

Batch 4EB3

Batch 5Pre-Val

DissolutionNot less than 80% (Q) of the labelled amount is dissolved

in 45 minutes.

1 98 92 90 90 92

2 94 97 94 97 102

3 96 99 87 101 94

4 104 98 88 90 96

5 95 94 102 97 84

6 99 97 100 85 93


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Process Performance Based on Cpk Values

Batch Cpk YieldProcess

Performance (y)(yi-ybar)^2

Batch 1 1.23 98.6 0.986 0.0086

Batch 2 2.03 100.0 1.000 0.0112

Batch 3 0.91 88.5 0.885 0.0001

Batch 4 1.17 97.7 0.977 0.0071

Batch 5 0.60 61.8 0.618 0.0756

Mean = = 0.8930 Var = Syy= 0.02566


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Estimation of Parameters

•

Y bar 0.8930

Syy 0.0257

Alpha Estimated 2.43

Beta Estimated 0.29

Y = (Y1…….YJ) denotes the Dissolution data of batches produced during stage1 PD


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Estimation of Parameters

n 3.11 4

4 qualification batches should be used for stage2 PPQ

If n=4, the posterior probability for a future batch produced after a successful stage2 PPQ to pass specification is = 0.96


13

FTO


CPV Phase 1 Risk Assessment

CPV Phase 2P

P

Q

C

O

M

M

E

R

C

I

A

L

Stage 3Continuous Process Verification

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FTO

CPV is a formal procedure which enables detection of a variation in the manufacturing

process that might have impact on the product quality / process consistency

CPV should

- Evaluate that a Process consistently delivers product with

acceptable QA’s & continues to operate robustly, within the

validated state.

- Identify any new sources of variability in the process that may

have arisen since the stage 2 (PQ stage) was performed

Continuous Improvement

Variation


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Continuous Improvement

Variation

FTO

CPV is a formal procedure which enables detection of a variation in the manufacturing

process that might have impact on the product quality / process consistency

CPV Scope

- Since we have limited data when commercial production starts, it is recommended that

CPV analysis is performed in two phases

CPV Phase 1- Pre-SPC

CPV Phase 2- SPC Phase


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FTO

CPV Phase 1 – Pre SPC

- Process performance can be analysed on limited data set (including PPQ, Submission & Optimization batches) to get understanding of the normal process variability in the commercial facility.

- At the completion of Phase 1 the alert limits to be established and also risk assessment to be relooked which was performed at end of PPQ batches to determine whether it has changed the risk score. And whether parameters not originally included in the plan for the initial CPV phase needs to be added.

Optimization Batches

Submission Batches

PPQ & Commercial

Batches

10 – 30 Batches


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FTO

▪ Batch Release and Trends

▪ OOS/OOT/Incidents/Deviations

▪ CAPA / Change Control

PQR (Document) (Site Committee)

▪ Product Scorecard (Cp/CpK, other

tools)

▪ CTQ & Risk Document

Product

Information

Real Time Batch

Wise Review

Product Quality Reviews

with frequency determined

by product risk

Statistical review (lean/ 6 sigma tools) of CTQ

data, PAT and CPPs when each batch is

manufactured. SQC to SPC

CPV Phase 2 – SPC Phase

- Ongoing verification of the process over the lifetime of the product lifecycle

- Identify trends which may be within the normal process variability but indicate a potential to trend outside the alert limits

- Continue to build the understanding of the sources of variability in the process & its impact.


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Process design

Only for CB/EB after PO

• Sampling plan based on confidence and reliability for each CQA (success run theorem or Z1.9)

• In process IPDU for blend and content uniformity

• Determine number of PPQ batches based on PilotBio, CB and EB batch data

Process performance qualification

• In process IPDU for blend and content uniformity

• Sampling plan - risk based approach for each CQA using EB/CB data

• Determine no of CPV I batches based on pre-validation and PPQ batch data

Continuous process verification – I

• In process IPDU

• Sampling plan - risk based approach for each CQA using pre-validation and PPQ data

• Risk assessment

Continuous process verification - II

• Sampling plan - risk based approach for each CQA using CPV I data

• Data monitoring

• Quality reporting

• CPV

Ris

k as

sess

me

nt

Ris

k as

sess

men

t

Eval

uat

ion

Conceptual application of QbD through out a products lifecycle.

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Conceptual application of QbD through out a products lifecycle.

QTPPProcess

SelectionPrior

Knowledge

Process Developm

ent

Design Space

Control Strategy

Continual Improvem

ent

Define Quality targets

based on Product

intended use

List the unit operations

in the process selected

Based on prior

knowledge-Initial Risk

Assessment to identify CQA & CPP

Perform developmen

t trials –DOE, PAT,

Risk Assessment

Summarize scientific

understanding of

material & process

Define Control strategy based on

design space created

Proposal for Life cycle

management basis of

Quality Risk Managemen

t.

DEVELOPMENT

MNUFACTURING

LIFECYCLE MANAGEMENT

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• QbD Enablers




Agenda

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QTPPProcess

SelectionPrior

Knowledge

Process Developm

ent

Design Space

Control Strategy

Continual Improvem

ent

Design Of Experiments (DOE)

Process Analyzers (PAT)

SPC, SQC & Acceptance Sampling

Process Modelling

Enab

ling

too

ls

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QTPPProcess

SelectionPrior

Knowledge

Process Developm

ent

Design Space

Control Strategy

Continual Improvem

ent

Design Of Experiments (DOE)

Enab

ling

too

ls

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DOE- Design Of Experiments

• Systematically chosen group of experiments where the levels of (chosen) process parameters are varied together to measure an effect on a Critical Quality Attribute (CQA)

– Some factors are controlled while others are held constant

– Basic Metrics

• Number of factors

• Number of runs

• Confidence and power

– Isolate effects including interactions

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DOE- Predicting Process Performance

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DOE- Developing Process Understanding

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DOE- Comparison of Experimental Environments

Characteristic Screening Characterization Optimization

No. of Factors >6 3-6 2-5

Desired Information

Critical FactorsUnderstand how

system works

Prediction Equation, Optimization,

Design Space

Model Form Linear or Main effectsLinear & Interaction

EffectsLinear, Interaction &

Curvilinear Effects

Experiment Design

Plackett-BurmanFractional-Factorials

DSD- Definitive screening design

Full & FractionalFactorials

Taguchi Orthogonal arrays, Split Plot

designs

Full & FractionalFactorials,

Response Surface

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QTPPProcess

SelectionPrior

Knowledge

Process Developm

ent

Design Space

Control Strategy

Continual Improvem

ent

Process Analyzers (PAT)

Enab

ling

too

ls

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PAT FRAMEWORK

FTO

DESIGN FOR

QUALITYSENSORS

PROCESS

CONTROLSDATA ANALYSIS

DOE, FMEA

Univariate, Multivariate, Real time

Soft- Temp, CFMDirect- NIR, Raman

Feedback, Feedforward

Its just not about NIRs

“PAT is considered to be a system for designing, analysing and controlling manufacturing through timeline measurements (i.e., during processing of critical quality and performance attributes of raw and in-process materials and processes

with the goal of ensuring final product quality)”

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PAT in Product Lifecycle Management

FTO

Process Qualificatio

n

Continuous Process

Verification

Product Design

PAT can be employed during the entire Product Life cycle for varying benefits

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FTO

Process Qualificat

ion

Continuous Process

Verification

Product Design

New Products- PAT utilized throughout development & Scale up.

Better understanding of Impact of CPP,CMA on the CQA


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FTO

Process Qualificat

ion

Continuous Process

Verification

Product Design

Commercial Products- PAT utilized for Analyzing CQA’s & Monitoring CPP

Step wise approach, first improve quality & then efficiency


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FTO

Process Qualificat

ion

Continuous Process

Verification

Product Design

Existing Marketed Robust Products-PAT utilized to improve efficiency.

Mechanism to keep ensuring product is in state of control

State of Control


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QTPPProcess

SelectionPrior

Knowledge

Process Developm

ent

Design Space

Control Strategy

Continual Improvem

ent

SPC, SQC & Acceptance SamplingEn

ablin

g to

ols

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Process Step-1

Process Step-2

Process Step-3

Product Output

Statistical Process Control Statistical Quality Control

Statistical quality control can be divided into three broad categories: 1. Descriptive statistics are used to describe quality characteristics and relationships for e.g. mean, standard deviation, the

range, and a measure of the distribution of data. 2. Statistical Process Control (SPC) involves inspecting a random sample of the output from a process and deciding

whether the process is producing products with characteristics that fall within a predetermined range. SPC answers the question of whether the process is functioning properly or not.

3. Acceptance Sampling is the process of randomly inspecting a sample of goods and deciding whether to accept the entire lot based on the results. Acceptance sampling determines whether a batch of goods should be accepted or rejected.

SPC & SQC

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Data Acquisition

• Acquire from different sources –Manual, Online data including from ERP, LIMS, e-BPRs, Analytical data etc.

Data Preparation

• Identification of Continuous, Discrete Data

• Identification of Outliers

• Parametric

• Non-Parametric Analysis

Data Analysis

• Basic Statistics

• Checking Data Distribution

• Data transformation (if necessary)

• Plotting Control Charts

• Run tests

• Process Capability Analysis

Control Charts & Process Capability Estimation

SPC & SQC- A Roadmap

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Data Acquisition

• Acquire from different sources – Manual, Online data including from ERP, LIMS, e-BPRs, Analytical data etc.

“How the data is collected is at least as important as the data themselves”

Remember Garbage in= Garbage Out

- Start with a clear objective

- Use Validated Measurement Methods

- Calibrated instruments

- Well defined and documented operating procedures

- Lack of experimenter bias

- Scientific based selection and assignment of samples

- Traceability

- Chain of Custody

- Well- written signed protocol


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Data Preparation

• Identification of Continuous, Discrete Data

• Identification of Outliers

• Parametric

• Non-Parametric Analysis

“First, Last and Always:

Plot your data.”

A lot of the Statistical tests and treatments we use ‘assume’. Always check assumptions.

Use: • Histograms

• Scatter Plots

• Probability Plots

10610410210098969492

105.0

102.5

100.0

97.5

95.0

S3

S1

Scatterplot of S1 vs S3

10410210098969492

20

15

10

5

0

S3

Fre

qu

en

cy

Mean 100.2

StDev 2.213

N 77

Histogram of S3Normal


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Data Analysis

• Basic Statistics

• Checking Data Distribution

• Data transformation (if necessary)

• Plotting Control Charts

• Run tests

• Process Capability Analysis

Identify Outliers using Box Whisker plots.

Correlate with OOS, OOTs, Deviations history, relevant CAPAs and then cleanse these points before doing any further analysis.

Basic Statistics

Mean, Median, Mode, Frequency of Mode, %RSD/ Coefficient of Variation, Range, Maxima, Minima

Tell a lot! Use them!


Control Charts & Process Capability Estimation

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SPC & SQC- Control Charts

Potential Applications-- To proactively monitor and trend a process- To detect the presence of special cause

variation- To identify continual improvement

opportunities- To maintain the process in the state of

statistical control

Control Chart- It’s a graphical display of a product quality characteristic that has been measured or computed periodically from a process at a defined frequency.

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• Process capability is the ability of the process to meet the design specifications for a service or product.

• Nominal value is a target for design specifications.

• Tolerance is an allowance above or below the nominal value.

SPC & SQC- Process Capability

Centering –The Process Is On Target

Spread – Reduce The Variation

LSL USL

DefectsDefects

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Introduction to Sampling

• Sampling is the process of selecting predetermined number of units from a population of interest.

• By studying the sample we may fairly generalize our results back to the population from which they were chosen.

• The sample should be a representation of the entire population.

• When taking a sample from a larger population, it is important to consider how the sample is chosen and how many samples to be collected.

Acceptance Sampling

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Sampling Risks

• Sampling involves risks:

- Good product/lot may be rejected

- Bad product/lot may be accepted

Because we inspect only a sample, not the whole lot.

Acceptance Sampling

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• Producer’s Risk: Risk associated with a lot of acceptable quality rejected

Denoted with - Alpha (α) – 5% is common= Prob (Committing Type I error)

= P (Rejecting a lot at AQL quality level)

= Producer’s risk

• Consumer’s Risk: Receive Shipment, assume good quality, but it is actually bad quality

Denoted with – Beta (β) – 1% is typical value= Prob (Committing Type II error)

= P (Accepting a lot at RQL quality level)

= Consumer’s risk

The OC Curve for a sampling plan quantifies these risks

Acceptance Sampling

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Why Not 100% Inspection

• Very Expensive

• Can’t use when product must be destroyed to test

Hence 100% inspection is tedious and difficult. So we inspect/test sample of few items taken from the lot and generalize our results to entire lot.

? Then the obvious question comes in our mind that, what is the suitable sample size required in order to well represent the entire lot…

Acceptance Sampling

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Determine Sample Size

• Determining sample size is a very important issue because –

- Samples that are too large may waste time, resources and money.

- While samples that are too small may lead to inaccurate results.

• Many times there will be two situations to determine sample size.- Calculating sample size without historical data

- Calculating sample size with historical data

Acceptance Sampling

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Scenario 1: Calculating sample size without historical data - Success Run Theorem

• The success run theorem uses the confidence level (how sure we are) and reliabilityvalue (valid, consistent results) to determine the appropriate statistically valid sample size for process validation.

• Before we start success run theorem method, we must establish our definitions of risk and their associated confidence level and reliability value. These definitions will vary based on organizational needs.

• The better way of determining the risk level is failure mode and effect analysis (FMEA).

Acceptance Sampling

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• Below are general definitions of risk in relation to patient safety.

Risk Definition

High Life threatening or may result in death

Medium

Many result in temporary or permanent injury

requiring medical intervention

Low May result in minor injury or inconvenience

not requiring medical intervention.

Acceptance Sampling

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• If the product is medical device, The following can be used as a guidelines to establish confidence and reliability levels based on patient risk.

• Different confidence and reliability levels can be utilized based on the organizational risk acceptance and industry practice, guidance documents and regulatory requirements.

Risk Confidence Reliability

High 95% 99%

Medium 95% 95%

Low 95% 90%

Acceptance Sampling

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Success Run Theorem Method:

• When calculating the sample size based on confidence and reliability with zero failures, we can use the below formula:

Where:

Ln = Natural log

n = Sample size

C = Confidence level

R = Reliability

n = ln(1−𝐶)

ln(𝑅)

Acceptance Sampling

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Example to calculate sample

size (using SRT) at finished stage.

Product: XYZ Cap (Batch Size : 500000 Capsules)

* For the case above with 59 samples (95% confident that a process is 95% reliable) we can state that we are 95% confident the true defect rate is between 0 and 5%.

Critical Quality Attributes - Finished Product Stage

Sno Attribute Type of attribute Safety/Quality/Efficacy

Criticality level of the attribute

Sample Size

1 Description Quality Low 29

2 Loss on drying Quality Low 29

3 Drug Release: Acid Stage Efficacy High 299

4 Drug Release: Basic Stage Efficacy High 299

5 Assay Efficacy High 299

6 Omeprazole related compound F and G

Safety Medium 59

7 5-methoxy-1 H-benzimidazole-2-thiol Safety Medium 59

8 Any Other individual impurity Safety Medium 59

9 Total impurities Safety High 299

1431

Acceptance Sampling

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Product: ABC tabs (Batch Size : 200000 Tablets)

Critical Quality Attributes - Finished Product Stage

Sno Attribute Criticality level of the attribute Sample Size

1 Uniformity of Dosage Units High 299

2 Assay High 299

3 Dissolution Medium 59

4 Related Substances Low 29

686

Acceptance Sampling

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OC Curve:

• This is a graph used in quality control to determine the probability of accepting production lots when using different sampling schemes.

• It shows percentage defectives on the X-axis and probability of acceptance on the Y-axis.

• Lots having more than the acceptable percentage of defectives are rejected.

• It helps in the selection of sampling plans that are effective in reducing risk.

Acceptance Sampling

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OC Curves with different Sample Sizes (Acceptance Number c=0)

Sample Size 29 59 299

Acceptable No. 0 0 0

Percent Defective Low Medium High

0.00 1.00 1.00 1.00

0.05 0.23 0.05 0.00

0.10 0.05 0.00 0.00

0.15 0.01 0.00 0.00

0.20 0.00 0.00 0.00

0.25 0.00 0.00 0.00

0.30 0.00 0.00 0.00

0.35 0.00 0.00 0.00

0.40 0.00 0.00 0.00

0.45 0.00 0.00 0.00

0.50 0.00 0.00 0.00

0.55 0.00 0.00 0.00

0.60 0.00 0.00 0.00

0.65 0.00 0.00 0.00

0.70 0.00 0.00 0.00

0.75 0.00 0.00 0.00

0.80 0.00 0.00 0.00

0.85 0.00 0.00 0.00

0.90 0.00 0.00 0.00

0.95 0.00 0.00 0.00

1.00 0.00 0.00 0.00

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10P

rob

abili

ty o

f ac

cep

tan

ce (

Pa)

Percent Defective

OC Curve

Low

Medium

High

Sample SizeProbability of Acceptance

0.1 0.25 0.5 0.75 0.9 0.95 0.9929 7.00% 4.70% 3.00% 1.50% 0.60% 0.30% 0.10%

59 4.50% 3.60% 2.30% 1.20% 0.50% 0.30% 0.10%299 4.20% 3.30% 2.20% 1.20% 0.50% 0.30% 0.10%

Acceptance Sampling

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Scenario 2: Calculating Sample Size with Historical Data - Risk Based Approach

• Formula to calculate Sample size:

n = (Zα + Zβ)2 S2 / ∆2

• Where: n = Sample Size (Need to be calculated)

Zα = Type I error rate

Zβ = Type II error rate

∆ = Standard deviation shift

S = Process + Measurement variability

Confidence

LevelΒ

Α

1 - Sided 2 – Sided

80% 0.842 0.842 1.282

90% 1.282 1.282 1.645

95% 1.645 1.645 1.96

98% 1.96 1.96 2.241

99% 2.326 2.326 2.576

100% 2.576 2.576 2.807

Values of Alpha and Beta for various Confidence Levels

Acceptance Sampling

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Sample data for Finished Product Analysis - Example

Test Specification Batch 1 Batch 2 Batch 3 Batch 4

Uniformity of Dosage Units

The Acceptance value (AV) should be less than or equal to 15.0

Sno Value1 101.72 100.53 100.94 100.65 99.86 100.27 100.98 100.09 96.4

10 99.4AV= 3.5 AV= 3.4 AV= 2.5 AV= 2.7

Acceptance Sampling

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Sample Size Calculation - Uniformity of Dosage Units

Uniformity of Dosage Units

n = (Zα + Zβ)2 S2 / ∆2

Criticality Level Type I Error Rate α - Value Type II Error Rate β - Value SD (S) Delta (∆) Sample Size (n)

High 5% | 95% 1.96 1% | 99% 2.326 1.43 0.5 150

Acceptance Sampling

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Comparison: With and Without Historical Data

Risk Based Approach – Product A n = (Zα + Zβ)2 S2 / ∆2

CQA Criticality Level Type I Error Rate α - Value Type II Error Rate β - Value SD (S)Delta

(∆)Sample Size (n)

Uniformity of Dosage

High 5% | 95% 1.96 1% | 99% 2.326 1.43 0.5 150

Success Run Theorem – Product A n = ln(1 - C)/ln( R )

CQA Criticality Level Confidence Reliability Sample Size (n)

Uniformity of DosageHigh 95% 99% 299

Acceptance Sampling

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• QbD Enablers




Agenda

59

CPV Monitoring Plan- Legacy Products

Review known CQA’s

Process Robust &

statistically capable

Conduct retrospective product risk assessment

Document the statistical assessment & review

frequency

Review Product- CMA- Control Strategy (CPP)- In Process Controls- Quality system Indicators (Mkt

complaint)- Environment Condn’s

Priority Matrix

Review & Identify additional elements for enhanced monitoring

Establish Process Performance

Process Robust &

statistically capable

Database creation & Data collection

YES

YES

NO

NO

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CPV Monitoring Plan- New Products

Stage 1Process

Development

CAPA required

Continuous Improvement &

Change management

Establish Stage 3 Monitoring / Sampling Plan

Assess need for enhanced understanding of

- Criticality level of Quality attribute

- Process Robustness

Periodic Review

Database collection& Data Review

YES

NODocument Control Strategy- CQA, CPP

Stage 2 PPQ batches

Verify Control Strategy

Minor

Major

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Flow for the Critical to Quality (CTQ) template

List of CQA’s

• Provides a list of Critical Quality Attributes along with the type and criticality level for the attributes for both Finished Product and Intermediates

Impact of CPP/CMA on CQA

•Evaluates impact of CMA of API on CQAs (High/Medium/Low)

•Evaluates impact of CPP of both Intermediates and Finished Product on CQAs (High/Medium/Low)

Deep dive of impact of

CMA on CQA & CS

•Provides justification for criticality of CMA for CQA

CPP & Control Strategy

•Evaluates in detail impact of CPPs and their control strategy value

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List of CQAs

Level 1A : Critical Quality Attributes - Finished Product (Coated Tablet)

S. No. AttributeType of attribute

Safety/Quality/EfficacyCriticality level of the attribute

1 Description Quality Medium2 Odor Quality Low3 Identification Quality Medium4 Assay (%w/w) Efficacy High

5 Content uniformity by UOD Efficacy High

6 Dissolution profile (%) Efficacy High

7 Related Substances limit (%w/w) Safety High

8 Anti-oxidant potency (%w/w) Quality Medium

9 Water content (%w/w) Quality High

10 Residual Solvents (%w/w) Safety Medium

11 Solid state nature of API in product Quality High

12 Microbial enumeration Safety Medium

Note: In similar lines CQA for intermediates will also be listed

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Impact of CMA on CQAs

Level 2A: Evaluation of impact of CMA of API & RM on CQA

S.No.Assay

(%w/w)

Related Substances

(%w/w)

Content uniformity by

UOD

Dissolution profile (%)

Water content (%w/w)

Solid state nature of API

in product

1 API Low Medium Low Low Low High2 Copovidone (Kollidon VA64) Low Medium Low Low Low Low

3 Butylated HydroxyToluene Low Low Low Low Low Low

4 Butylated Hydroxy Anisole Low Low Low Low Low Low

6 Methanol Low Low Low Low Low Low

7Polyethylene Oxide (PEO WSR303) Low Low Low High Low Low

8 Polyethylene Glycol (PEG 8000) Low Low Low Low Low Low

9 Hypromellose (HPMC K100 LVCR) Low Low Low High Low Low

10 Magnesium Stearate Low Low Low Low Low Low

11 Hypromellose (HPMC E5 LV) Low Low Low Low Low Low

12 Polyethylene Glycol NF Low Low Low Low Low Low

13 Isopropyl Alcohol Low Low Low Low Low Low

14 Opadry 200 Yellow (for 50 mg ) Low Low Low Low Low Low

15 Opadry 200 Brown (for 25 mg) Low Low Low Low Low Low

16 Purified Water Low Low Low Low Low Low

CQACMA

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Impact of CPP on CQAsLevel 2B: Evaluation of impact of CPP on CQA

Finished Product CQA

S.No.Assay

(%w/w)

Related Substances

(%w/w)

Content uniformity

by UOD

Dissolution profile (%)


Solid state nature of

API in product

1 Sifting Low Low Low Low Low Low

2 Drug-binder solution preparation Low Low Low Low Low Low

3 Dry mixing Low Low Low Low Low Low4 Fluid bed granulation Low Low Low High Low High6 Granules drying Low Low Low Low Low Low7 Milling Low Low Low Low Low Low8 Blending (Pre-lubrication) Low Low Low Low Low Low9 Blending (Lubrication) Low Low Low Low Low Low

10 Compression Low Low Medium High Low Low

11Seal & Film Coating solution preparation

Low Low Low Low Low Low

12 Seal coating Low Low Low Low Low Medium13 Film coating Low Low Low Low Low High14 Seal & Film coating drying Low Low Low Low Low Low

CPP

CQA

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Deep dive of impact of CMA on CQA & CSLevel 2A: Evaluation of impact of CMA on CQA

S.No.Specificat

ionAssay

(%w/w)

Related Substances (%w/w)

Content

uniformity by

UOD

Dissolution

profile (%)


Solid state

nature of API in

product

Justification for criticality (only for High/Medium)

1Water content (API)

NMT 4.0% w/w

Low Medium Low Low Low High

(I) API is hygroscopic in nature and is prone to hydrolytic degradtion as evident from the API forced degradation study and this may impact the related substance of the product. Hence the risk is rated as Medium.

(II) Impact of water content of active on retaining the input polymorphic form of API is considered as high, because API is hygroscopic in nature and polymorphic conversion may take place due to change in the water content of API. However API water content will be controlled through API specification. Hence the risk is High.

2

Limit of Peroxides (Copovidone)

NMT 0.40% w/w

Low Medium Low Low Low Low

The drug substance is prone to oxidation. Peroxide content may trigger oxidation of drug substance which expedite the impurity generation due to oxidation. The impurities likely have to impact on safety of product. Hence, the risk is medium.

3

Viscosity (HPMC K100 LVCR)

NLT 80 & NMT 120

mPas

Low Low Low High Low Low

The viscosity hypromellose depends on polymer parameters like molecular weight, hydrophilicy etc. Drug release through polymer matrix is inversely proportional to viscosity of hypromellose. The viscosity of selected grade of polymer is 80-120 mPas. The lot of hypromellose used for development trials had viscosity of 98 mPas and 102 mPas. The viscosity of hypromellose towards extremity of specification (towards lower side and higher side of the specification) will have impact on drug through polymer matrix. Hence, it is rated as High.

CQA

CMA

66

CPP and Control StrategyLevel 4: Detailed evaluation of impact of critical process parameter & its Control strategy value

Unit Operation Parameter Value (Lab scale) UOM

Fluid bed granulation

Spray rate 36 (15-60) g/min

Airflow 100 (80-140) CFM

% inlet RH 5 -55 %

Atomization pressure 1 (0.8-1.2) bar

Granules dryingInlet temperature 25-45 ˚C

EXhaust temperature 25-30 ˚C

Milling Milling speed 1200 (1200 -1700) RPM

Blending (Lubrication) Blending time 5 min.

Compression

Precompression force 10% of MCF kN

Main compression force (MCF) 20-Oct kN

Turret speed 20-40 RPM

Seal coatingSpray rate 2-15 g/min

Atomization pressure 1-2 bar

Film coating

Spray rate 1-5 g/min

Atomization pressure 2-3 bar

Inlet temperature 30-65 ˚C

EXhaust temperature 35-45 ˚C

67


• QbD Enablers




Agenda

68

Quality Metrics in Process Validation

• Create quality score card at various stages of product lifecycle

• Track quality using quality metrics

70

• Capable of being deployed for all products and compare products• Same basis of evaluation• Capture all key patient centric parameters• Serve as a basis for taking preventive action – Lead indicator• Quantified and based on statistically appropriate concepts

Create Product Quality Score cards

71

For Qualitative data

Upper limit = 𝑈𝐿 =𝑣1∗𝐹

𝑣2+𝑣1∗𝐹

Wherev1 = 2(x+1)v2 = 2(n-x)x = number of nonconformancen = number of batchesF = lower α/2 point of F with v1 and v2 degrees of freedomNote: when x=0 or x=n, calculate the one-sided confidence interval.

Converted to individual score based on the 95% upper confidence limits on percent non-conformance using (1-UL)*100

For Quantitative data following robustness score is used for calculation

Ppk Range Score Range

Ppk <= 1 0 – 25

1 < Ppk <= 1.33 26 – 50

1.33 < Ppk <= 1.67 51 – 75

Ppk > 1.67 75 – 100

Product Scorecard Concept & Key Attributes

72

Process Engineering Update

Use Non parametric PpK to compute an overall Product score

Method:Continuous data: Non parametric calculation of PpK using medians and percentiles Correction Factor assign scoreDiscrete data: Calculate failure rate UL of Non-conformance assign score

• Independent of normality assumption, batch size

• Sensitive to variability & closeness to specs

Product Scorecard Concept & Key Attributes

Ppk Range Score Range

Ppk <= 1 0 – 25

1 < Ppk <= 1.33 26 – 50

1.33 < Ppk <= 1.67 51 – 75

Ppk > 1.67 75 – 100

73

Process Engineering Update

Product Scorecard – Online Tracking

85

87

89

91

93

95

97

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Water by KF

0

1

2

3

4

5

6

7

8

9

Assay UOD

Product Score

Score – CQA ( High)

Score – CQA ( Medium)

The process is behaving normally

& within limits as indicated by

the product score and the CQAs,

the advantage of this

methodology is capturing the

performance at a ‘batch level’

and thus capturing information

on an almost real time basis and

thus enabling the organization to

take pre-emptive/ corrective

action wherever necessary

The process though has not

produced any OOS, even an out

of trend/ extreme observation is

magnified and highlighted to the

management for taking

appropriate corrective action.

74


• QbD Enablers




Agenda

75

Pharmaceutical Development Background

• API Characteristics– Relatively High dose compound

– High Water Solubility, Moderate Permeability

• Formulation Design– Extended Release Formulation

– Level A IVIVC has been established for all dose strengths

• Process Design– Roll Compactor process

QbD Case study – Example

76

Quality Risk Assessments

• Product CQA’s– Risk assessment focusing on Voice of Customer applied to propose CQA’s

• Material Attributes & Process Parameters– FMEA applied

– Critical & Non Critical Process parameters were identified

– Manufacturability were addressed

– DoE performed at small scale to study potential CPP’s

– DoE applied for validation at full scale


77

Risk Prioritization Matrix


78

Potential CPPs and MAs


79

Roller Compactor Design Space

• Developed using DoE at small scale

• Roll force and Roll gap– No impact on the performance of the drug product (no change in dissolution

profiles observed)

– Impact manufacturabity of the product (flow issues)

• Scale up factors used to predict large scale design space

• Relationship of equipment & scale and design space was addressed


80

DoE used for scale up


81

Design space confirmed at full scale


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